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Related Concept Videos

Nucleic Acid Structure01:25

Nucleic Acid Structure

The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Erwin Chargaff’s rules on DNA equivalence paved the way for the discovery of base pairing in DNA. Chargaff’s rules state that in a double-stranded DNA molecule,
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Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
Nucleic Acids02:43

Nucleic Acids

Nucleic acids are the most important macromolecules for the continuity of life. They carry the cell's genetic blueprint and carry instructions for its functioning.
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The two main types of nucleic acids are deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). DNA is the genetic material in all living organisms, ranging from single-celled bacteria to multicellular mammals. It is in the nucleus of eukaryotes and in the organelles, chloroplasts, and mitochondria. In prokaryotes, the...
Nucleic acids02:43

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Visualization and Quantification of Intermolecular RNA Base Pairing in in vitro RNA Clusters Using Split Broccoli RNA Reporters
10:52

Visualization and Quantification of Intermolecular RNA Base Pairing in in vitro RNA Clusters Using Split Broccoli RNA Reporters

Published on: May 29, 2026

Partition function and base pairing probabilities for RNA-RNA interaction prediction.

Fenix W D Huang1, Jing Qin, Christian M Reidys

  • 1Center for Combinatorics, LPMC-TJKLC, Nankai University Tianjin 300071, P.R. China.

Bioinformatics (Oxford, England)
|August 13, 2009
PubMed
Summary
This summary is machine-generated.

We developed a new dynamic programming algorithm to calculate RNA-RNA interaction structures and their probabilities. This method efficiently models the ensemble of structures crucial for RNA binding thermodynamics.

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Last Updated: Jun 21, 2026

Visualization and Quantification of Intermolecular RNA Base Pairing in in vitro RNA Clusters Using Split Broccoli RNA Reporters
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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Molecular Biology

Background:

  • The RNA-RNA interaction problem (RIP) involves determining the optimal structure of two binding RNA molecules.
  • RNA binding, similar to RNA folding, is influenced by a vast ensemble of structures, not just the ground state.
  • Existing dynamic programming algorithms address RIP but may not fully capture the thermodynamic contributions of alternative structures.

Purpose of the Study:

  • To present a novel dynamic programming algorithm for computing the partition function of RNA-RNA interactions.
  • To enable the calculation of base pairing probabilities for joint RNA structures.
  • To provide a computational tool for investigating RNA-RNA interactions, particularly for small RNAs and mRNAs.

Main Methods:

  • Developed an O(N^6) time and O(N^4) space dynamic programming algorithm based on 'tight structures'.
  • Utilized a decomposition tree approach for joint structures to compute base pairing probabilities.
  • Implemented the algorithm in C, creating the 'rip' software.

Main Results:

  • The algorithm computes the full partition function for RNA-RNA interactions.
  • Successfully calculates base pairing probabilities for joint RNA structures.
  • The implementation is practically efficient for analyzing interactions of biological relevance, such as small bacterial RNAs with target mRNAs.

Conclusions:

  • The new algorithm provides a comprehensive method for analyzing RNA-RNA interactions.
  • The ability to compute partition functions and base pairing probabilities enhances understanding of RNA binding thermodynamics.
  • The 'rip' software offers a valuable tool for researchers studying RNA-RNA interactions in biological systems.